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rojasgabriel merged 1 commit into from
Aug 3, 2026
Merged

Unify psychophysical kernel timing sources #10

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4 changes: 3 additions & 1 deletion README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -44,8 +44,10 @@ Chipmunk data. Archived `djchurchland` notebooks are under
```bash
uv run python scripts/analyses/seed_behavior_analysis_set.py --help
uv run python scripts/analyses/migrate_behavior_analysis_schema.py --help
uv run python scripts/analyses/migrate_kernel_timing_source.py --help
uv run python scripts/analyses/populate_behavior_tables.py --help
uv run python scripts/analyses/plot_psychometrics.py --help
```

Use `--dry-run` on seed/populate before any database writes.
Seed and populate support `--dry-run`. Schema migrations print their planned
changes unless explicitly run with `--apply`.
34 changes: 32 additions & 2 deletions docs/MIGRATION.md
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Expand Up @@ -47,8 +47,8 @@ and **Plan Analysis** v0.5.
| `PsychometricFitConfig` | Lookup | One versioned psychometric eligibility configuration | none | minimum choices/stimulus values, analysis version |
| `PsychometricSessionFit` | Computed | One fit for one upstream TrialSet and config | `DecisionTask.TrialSet` + config | status, fit sample size, curve/parameters/diagnostics |
| `PsychometricSubjectFit` | Computed | One pooled fit for one analysis set, subject, condition, and config | analysis set + `Subject` + config | status, fit sample size, curve/parameters/diagnostics |
| `PsychophysicalKernelFitConfig` | Lookup | One versioned pooled-kernel configuration | none | bins, CV folds, seed, calibration rate, regularization, version |
| `PsychophysicalKernel` | Computed | One pooled kernel for one analysis set, subject, condition, and config | analysis set + `Subject` + kernel config | status, fit sample size, weights, held-out scores, bias |
| `PsychophysicalKernelFitConfig` | Lookup | One versioned pooled-kernel configuration | none | binning method, observation window, evidence encoding, CV, regularization, version |
| `PsychophysicalKernel` | Computed | One pooled kernel for one analysis set, subject, condition, and config | analysis set + `Subject` + kernel config | status, timing source, fit sample size, weights, held-out scores, bias |

Keep:

Expand Down Expand Up @@ -130,3 +130,33 @@ sizes, frameless legends, and vector output. Raw figures, commands,
configuration IDs, and observation-first notes are on the LAB-TASKS-479
[Results](https://www.notion.so/3b1ecf086b7c814a959aebd29420b453)
subpage.

## Kernel timing-source extension

The canonical pooled kernel now supports fixed 100 ms windows in addition to
the migrated variable-width `v1_10bin_10fold` fit. For fixed-window configs,
each selected session:

1. uses NIDAQ/OneBox visual-flash and port-event times when a `visual_stim`
`EventMapping` exists and all required mappings validate;
2. otherwise uses the equivalent Bpod `stim_events`, `t_react`, and
`t_response` timestamps;
3. raises on incomplete or invalid NIDAQ mappings rather than silently falling
back to Bpod.

`PsychophysicalKernel.timing_source` records `nidaq`, `bpod`, or `mixed` for the
pooled result. Detailed session provenance remains derivable from
`BehaviorAnalysisSet.TrialSet` and the ephys `EventMapping`; no duplicate
per-session provenance table is needed.

Before importing this version against the live schema, run:

```bash
uv run python scripts/analyses/migrate_kernel_timing_source.py
uv run python scripts/analyses/migrate_kernel_timing_source.py --apply
```

The first command is read-only and prints the missing `ALTER TABLE` statements.
The second adds the new nullable/defaulted columns and inserts the two v2
fixed-window configs while preserving all existing v1 rows as Bpod-timed
legacy fits.
238 changes: 197 additions & 41 deletions labdata_plugin/analysisschema.py
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Expand Up @@ -158,9 +158,57 @@ class PsychophysicalKernelFitConfig(dj.Lookup):
random_state : int
max_rate_hz : float # calibration rate
regularization_c : float
kernel_method = 'legacy_variable' : enum('legacy_variable', 'fixed_window')
bin_width_s = NULL : float
evidence_encoding = NULL : enum('max_rate', 'trial_rate')
min_trials_per_bin = NULL : int
observation_window = NULL : enum('center_exit', 'response')
analysis_version : varchar(32)
"""
contents = [("v1_10bin_10fold", 10, 10, 0, 20.0, 1.0, "v1")] # noqa: RUF012
contents = [ # noqa: RUF012
(
"v1_10bin_10fold",
10,
10,
0,
20.0,
1.0,
"legacy_variable",
None,
None,
None,
None,
"v1",
),
(
"v2_100ms_10bin_center_rate",
10,
10,
0,
20.0,
1.0,
"fixed_window",
0.1,
"trial_rate",
50,
"center_exit",
"v2",
),
(
"v2_100ms_10bin_response_rate",
10,
10,
0,
20.0,
1.0,
"fixed_window",
0.1,
"trial_rate",
50,
"response",
"v2",
),
]


@rojasbowe_schema
Expand All @@ -175,14 +223,21 @@ class PsychophysicalKernel(dj.Computed):
---
fit_status : enum('fit', 'skipped')
fit_message = NULL : varchar(256)
timing_source = 'bpod' : enum('nidaq', 'bpod', 'mixed')
n_trials_fit : int
n_bins_fit = NULL : int
n_observed_per_bin = NULL : longblob
bin_centers_s = NULL : longblob # time from first flash
weights = NULL : longblob # cv fold x stimulus time bin
weights_mean = NULL : longblob
weights_error = NULL : longblob
scores = NULL : longblob # held-out accuracy by fold
score_mean = NULL : float
majority_accuracy = NULL : float
score_above_majority = NULL : float
bias = NULL : longblob # intercept by fold
bias_mean = NULL : float
interpretation = NULL : varchar(32)
"""

@property
Expand All @@ -194,51 +249,16 @@ def key_source(self):
return subject_conditions * PsychophysicalKernelFitConfig()

def make(self, key):
from behavior_analyses.io import get_chipmunk_table
from behavior_analyses.kernels import fit_psychophysical_kernel

config = (PsychophysicalKernelFitConfig() & key).fetch1()
trialset_keys = _selected_trialset_keys(key)
Chipmunk = get_chipmunk_table()
relation = (
Chipmunk() * Chipmunk.Trial() * Chipmunk.TrialParameters()
& trialset_keys
& {"rewarded_modality": key["trialset_description"]}
)
stim_events, response_values = relation.fetch("stim_events", "response")
result = fit_psychophysical_kernel(
stim_events,
response_values,
timebins=int(config["timebins"]),
cv_splits=int(config["cv_splits"]),
random_state=int(config["random_state"]),
max_rate_hz=float(config["max_rate_hz"]),
regularization_c=float(config["regularization_c"]),
)
n_trials_fit = int(result["choice_right"].size)
if result["weights"].size == 0:
self.insert1(
{
**key,
"fit_status": "skipped",
"fit_message": "insufficient trials or response classes for CV",
"n_trials_fit": n_trials_fit,
}
)
return

if config["kernel_method"] == "legacy_variable":
payload = _legacy_kernel_payload(key, config, trialset_keys)
else:
payload = _fixed_kernel_payload(key, config, trialset_keys)
self.insert1(
{
**key,
"fit_status": "fit",
"n_trials_fit": n_trials_fit,
"weights": result["weights"],
"weights_mean": np.mean(result["weights"], axis=0),
"weights_error": np.mean(result["error"], axis=0),
"scores": result["scores"],
"score_mean": float(np.mean(result["scores"])),
"bias": result["bias"],
"bias_mean": float(np.mean(result["bias"])),
**payload,
}
)

Expand All @@ -264,6 +284,142 @@ def _fetch_trialset_rows_for_subject(key):
return list((DecisionTask.TrialSet() & trialset_keys).fetch(as_dict=True))


def _legacy_kernel_payload(key, config, trialset_keys):
from behavior_analyses.io import get_chipmunk_table
from behavior_analyses.kernels import (
fit_psychophysical_kernel,
interpret_kernel_weights,
)

Chipmunk = get_chipmunk_table()
relation = (
Chipmunk() * Chipmunk.Trial() * Chipmunk.TrialParameters()
& trialset_keys
& {"rewarded_modality": key["trialset_description"]}
)
stim_events, response_values = relation.fetch("stim_events", "response")
result = fit_psychophysical_kernel(
stim_events,
response_values,
timebins=int(config["timebins"]),
cv_splits=int(config["cv_splits"]),
random_state=int(config["random_state"]),
max_rate_hz=float(config["max_rate_hz"]),
regularization_c=float(config["regularization_c"]),
)
n_trials_fit = int(result["choice_right"].size)
if result["weights"].size == 0:
return {
"fit_status": "skipped",
"fit_message": "insufficient trials or response classes for CV",
"timing_source": "bpod",
"n_trials_fit": n_trials_fit,
"n_bins_fit": 0,
}

n_observed = np.full(int(config["timebins"]), n_trials_fit, dtype=int)
score_mean = float(np.mean(result["scores"]))
majority_accuracy = float(
max(np.mean(result["choice_right"]), 1.0 - np.mean(result["choice_right"]))
)
weights_mean = np.mean(result["weights"], axis=0)
return {
"fit_status": "fit",
"timing_source": "bpod",
"n_trials_fit": n_trials_fit,
"n_bins_fit": int(config["timebins"]),
"n_observed_per_bin": n_observed,
"weights": result["weights"],
"weights_mean": weights_mean,
"weights_error": np.mean(result["error"], axis=0),
"scores": result["scores"],
"score_mean": score_mean,
"majority_accuracy": majority_accuracy,
"score_above_majority": score_mean - majority_accuracy,
"bias": result["bias"],
"bias_mean": float(np.mean(result["bias"])),
"interpretation": interpret_kernel_weights(
weights_mean,
n_observed,
min_trials_per_bin=int(config["cv_splits"]),
),
}


def _fixed_kernel_payload(key, config, trialset_keys):
from behavior_analyses.kernel_timing import fetch_pooled_kernel_inputs
from behavior_analyses.kernels import (
build_fixed_residual_rate_matrix,
fit_fixed_psychophysical_kernel,
interpret_kernel_weights,
)

inputs = fetch_pooled_kernel_inputs(
trialset_keys,
key["trialset_description"],
observation_window=str(config["observation_window"]),
)
trial_rate_hz = (
inputs["trial_rate_hz"] if config["evidence_encoding"] == "trial_rate" else None
)
residual, choices, n_observed, bin_centers, expected_counts = (
build_fixed_residual_rate_matrix(
inputs["stim_times_per_trial"],
inputs["first_stim_times"],
inputs["observation_end_times"],
inputs["response_values"],
timebins=int(config["timebins"]),
bin_width_s=float(config["bin_width_s"]),
max_rate_hz=float(config["max_rate_hz"]),
trial_rate_hz=trial_rate_hz,
)
)
result = fit_fixed_psychophysical_kernel(
residual,
choices,
n_observed_per_bin=n_observed,
cv_splits=int(config["cv_splits"]),
random_state=int(config["random_state"]),
min_trials_per_bin=int(config["min_trials_per_bin"]),
regularization_c=float(config["regularization_c"]),
expected_counts=(
expected_counts if config["evidence_encoding"] == "trial_rate" else None
),
)
base = {
"timing_source": inputs["timing_source"],
"n_trials_fit": int(result["n_trials_fit"]),
"n_bins_fit": int(result["n_bins_fit"]),
"n_observed_per_bin": n_observed,
"bin_centers_s": bin_centers,
}
if not result["fit_converged"]:
return {
**base,
"fit_status": "skipped",
"fit_message": "insufficient trials or response classes for CV",
}

return {
**base,
"fit_status": "fit",
"weights": result["weights"],
"weights_mean": result["weights_mean"],
"weights_error": result["weights_error"],
"scores": result["scores"],
"score_mean": result["score_mean"],
"majority_accuracy": result["majority_accuracy"],
"score_above_majority": result["score_above_majority"],
"bias": result["bias"],
"bias_mean": result["bias_mean"],
"interpretation": interpret_kernel_weights(
result["weights_mean"],
n_observed,
min_trials_per_bin=int(config["min_trials_per_bin"]),
),
}


def _psychometric_fit_payload(row, config):
from behavior_analyses.psychometrics import fit_psychometric_labdata

Expand Down
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